


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
			import datasets

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							@slow
							@require_torch
							def        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														__snake_case						:     Optional[int]									=  EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"       ,      "prajjwal1/bert-tiny"							)
														__snake_case						:     int									=  BertTokenizer.from_pretrained("bert-base-uncased"							)

														__snake_case						:     List[str]									=  bertabert.config.encoder.vocab_size
														__snake_case						:     Dict									=  tokenizer.sep_token_id
														__snake_case						:     Dict									=  tokenizer.cls_token_id
														__snake_case						:     List[Any]									=  128

														__snake_case						:     Optional[int]									=  datasets.load_dataset("cnn_dailymail"       ,      "3.0.0"       ,      split="train[:1%]"							)
														__snake_case						:     str									=  datasets.load_dataset("cnn_dailymail"       ,      "3.0.0"       ,      split="validation[:1%]"							)

														__snake_case						:     str									=  train_dataset.select(range(32							)							)
														__snake_case						:     Union[str, Any]									=  val_dataset.select(range(16							)							)

														__snake_case						:     Union[str, Any]									=  4

														def _map_to_encoder_decoder_inputs(lowerCamelCase			:    int							):
																					# Tokenizer will automatically set [BOS] <text> [EOS]
																					__snake_case						:     Any									=  tokenizer(batch["article"]       ,      padding="max_length"       ,      truncation=lowerCamelCase       ,      max_length=512							)
																					__snake_case						:     Dict									=  tokenizer(batch["highlights"]       ,      padding="max_length"       ,      truncation=lowerCamelCase       ,      max_length=128							)
																					__snake_case						:     Optional[Any]									=  inputs.input_ids
																					__snake_case						:     List[str]									=  inputs.attention_mask

																					__snake_case						:     List[Any]									=  outputs.input_ids
																					__snake_case						:     Dict									=  outputs.input_ids.copy()
																					__snake_case						:     Tuple									=  [
																					    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
																					]
																					__snake_case						:     str									=  outputs.attention_mask

																					assert all(len(lowerCamelCase							) == 512 for x in inputs.input_ids							)
																					assert all(len(lowerCamelCase							) == 128 for x in outputs.input_ids							)

																					return batch

														def _compute_metrics(lowerCamelCase			:    Optional[Any]							):
																					__snake_case						:     List[str]									=  pred.label_ids
																					__snake_case						:     int									=  pred.predictions

																					# all unnecessary tokens are removed
																					__snake_case						:     List[Any]									=  tokenizer.batch_decode(lowerCamelCase       ,      skip_special_tokens=lowerCamelCase							)
																					__snake_case						:     Dict									=  tokenizer.batch_decode(lowerCamelCase       ,      skip_special_tokens=lowerCamelCase							)

																					__snake_case						:     str									=  sum([int(pred_str[i] == label_str[i]							) for i in range(len(lowerCamelCase							)							)]							) / len(lowerCamelCase							)

																					return {"accuracy": accuracy}

														# map train dataset
														__snake_case						:     Tuple									=  train_dataset.map(
														    _map_to_encoder_decoder_inputs       ,      batched=lowerCamelCase       ,      batch_size=lowerCamelCase       ,      remove_columns=["article", "highlights"]       ,      )
														train_dataset.set_format(
														    type="torch"       ,      columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]       ,      )

														# same for validation dataset
														__snake_case						:     Dict									=  val_dataset.map(
														    _map_to_encoder_decoder_inputs       ,      batched=lowerCamelCase       ,      batch_size=lowerCamelCase       ,      remove_columns=["article", "highlights"]       ,      )
														val_dataset.set_format(
														    type="torch"       ,      columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]       ,      )

														__snake_case						:     Optional[int]									=  self.get_auto_remove_tmp_dir()

														__snake_case						:     int									=  SeqaSeqTrainingArguments(
														    output_dir=lowerCamelCase       ,      per_device_train_batch_size=lowerCamelCase       ,      per_device_eval_batch_size=lowerCamelCase       ,      predict_with_generate=lowerCamelCase       ,      evaluation_strategy="steps"       ,      do_train=lowerCamelCase       ,      do_eval=lowerCamelCase       ,      warmup_steps=0       ,      eval_steps=2       ,      logging_steps=2       ,      )

														# instantiate trainer
														__snake_case						:     Optional[int]									=  SeqaSeqTrainer(
														    model=lowerCamelCase       ,      args=lowerCamelCase       ,      compute_metrics=_compute_metrics       ,      train_dataset=lowerCamelCase       ,      eval_dataset=lowerCamelCase       ,      tokenizer=lowerCamelCase       ,      )

														# start training
														trainer.train()





'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




